Service
AI Automation
The unglamorous option that pays back fastest.
The problem
A great deal of what gets pitched as AI is conditional logic with a language model bolted on for decoration. It costs more to run, it is harder to debug, and it fails in ways nobody predicted.
Deterministic, and monitored from day one
A trigger fires, deterministic logic runs, an action is taken. The part most projects leave out is monitoring, built before the automation rather than after, so a silent failure is caught by an alert rather than discovered a quarter later.
What we actually do
- Separate the parts of a workflow that need judgement from the parts that need rules.
- Automate the rules deterministically, where the output is the same every time.
- Reserve the model for the steps that genuinely need language or ambiguity.
- Build the monitoring before the automation, so a silent failure is not discovered a quarter later.
When this is the wrong answer. The process changes every month. Automating a moving target means rebuilding it every month. Stabilise the process first, or accept that the maintenance is the real cost.
Problems this solves